Quality assessment of volunteered geographic information for outdoor activities:an analysis of OpenStreetMap data for names of peaks in Japan

Geographical studies of outdoor activities have increased in recent years with the rise in popularity of these activities worldwide,including in Japan.Volunteered geographic information(VGI)is a key tool for organizing outdoor activities as it offers a means to determine the locational information and names of places.To evaluate the quality of VGI,geospatial data generated by land survey agencies and other VGI are often utilized as reference data.However,since these reference data may not be available,other methods are necessary to assure the quality of VGI.In this study,we examined five trust indicators based on the inherent characteristics of VGI through an empirical case study.We used mountain names extracted from OpenStreetMap in Japan as data because there were almost no other VGI in the vicinity.As a result,we isolated three trust indicators,namely versions,users,and tag corrections,to examine the thematic accuracy of VGI because these were the only statistically significant indicators.However,we found that the prediction rate of thematic accuracy was very low.To improve thematic accuracy,this study recommends using the most accurate versions,applying correctly given tags,and considering the motivations and characteristics of the VGI contributors.

Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information

In this geo-statistical analysis of change detection,we illustrate the evolution of the built-up environment in Shanghai at the street-block level.Based on two TerraSAR-X image stacks with 36 and 15 images,covering the city centre of Shanghai for the time period from 2008 to 2015,a set of coherence images was created using a small baseline approach.The road network from Open Street Map,a volunteered geographic information product,serves as the input dataset to create street-blocks.A street-block is surrounded by roads and resembles a ground parcel,a real estate property–a cadastral unit.The coherence information is aggregated to these street-blocks for each observation and the variation is analysed over time.An analysis of spatial autocorrelation reveals clusters of similar behaviours.The result is a detailed map of Shanghai highlighting areas of change.We argue that the aggregation and grouping of synthetic aperture radar coherence image information to real-world entities(street-blocks)is comprehensible and relevant to the urban planning process.Therefore,this research is a contribution to the community of urban planners,designers,and government agencies who want to monitor the development of the urban landscape.

On predicting and improving the quality of Volunteer Geographic Information projects

Initiatives that rely upon the contributions of volunteers to reach a specific goal are growing more and more with the success of Web 2.0–interactive applications.Also scientific projects are testing and exploiting volunteers’collaboration,but the quality of information obtained with this approach is often puzzling.This paper offers a rich overview of many scientific projects where geographic contributions are committed to volunteers,to the aim of defining strategies to improve information quality.By describing real examples of Volunteer Geographic Information(VGI),the contribution establishes a categorization based on the characteristics of the information,tasks,and scopes of the projects.After a discussion on the relationships of categories and VGI quality,the paper analyses techniques to improve the quality of volunteered information according to the moment of its assessment(i.e.,ex ante,ex post,or both with respect to information creation).The paper outlines the main limitations of the different approaches and indicates some guidelines for future developments.

Completeness of citizen science biodiversity data from a volunteered geographic information perspective

Observations of living organisms by citizen scientists that are reported to online portals are a valuable source of information.They are also a special kind of volunteered geographic information(VGI).VGI data have issues of completeness,which arise from biases caused by the opportunistic nature of the data collection process.We examined the completeness of bird species represented in citizen science observation data from eBird and iNaturalist in US National Parks(NPs).We used approaches for completeness estimation which were developed for data from OpenStreetMap,a crowdsourced map of the world.First,we used an extrinsic approach,comparing species lists from citizen science data with National Park Service lists.Second,we examined two intrinsic approaches using total observation numbers in NPs and the development of the number of new species being added to the data-set over time.Results from the extrinsic approach provided appropriate completeness estimations to evaluate the intrinsic approaches.We found that total observation numbers are a good estimator of species completeness of citizen science data from US NPs.There is also a close relationship between species completeness and the ratio of new species added to observation data vs.observation numbers in a given year.

A Method for Developing Seismic Intensity Maps from Twitter Data

Aim of this article, is to present a methodology for extracting macroseismic intensity information and producing seismic intensity maps from VGI （volunteered geographic information）. As a VGI source for obtaining and assessing macroseismic observations, the authors chose twitter. Our methodology is validated in two recent earthquakes occurred in Greece： the January 26, 2014 ML = 5.8 in Kefallinia, and the November 17, 2014 ML = 5.2 in Evoikos. Twitter data published within the first 6 h, 12 h, 24 h and 48 h after the earthquake occurrence were analyzed to develop seismic intensity maps. Those maps were evaluated through intensity maps for the same earthquakes, published by international institutes. Evaluation results provide a strong empiric evidence for the credibility of our methodology, the accuracy of the produced seismic intensity maps and accentuate VGI, generated by twitter, as an adequate alternative source for collecting macroseismic information.

Crowdsourcing geographic information for disaster response:a research frontier

Geographic data and tools are essential in all aspects of emergency management:preparedness,response,recovery,and mitigation.Geographic information created by amateur citizens,often known as volunteered geographic information,has recently provided an interesting alternative to traditional authoritative information from mapping agencies and corporations,and several recent papers have provided the beginnings of a literature on the more fundamental issues raised by this new source.Data quality is a major concern,since volunteered information is asserted and carries none of the assurances that lead to trust in officially created data.During emergencies time is the essence,and the risks associated with volunteered information are often outweighed by the benefits of its use.An example is discussed using the four wildfires that impacted the Santa Barbara area in 2007
2009,and lessons are drawn.

Accuracy and utility of the Structure Sensor for collecting 3D indoor information

This paper presents the result of an investigation into the utility of the Structure Sensor developed by Occipital Inc.and accuracy of its output for 3D surveying of interiors of buildings in relation to Surveying(Cadastral Survey)Regulation 2005 in Victoria,Australia.The paper investigates data acquisition issues,defines guidelines to obtain the best reconstruction result,and evaluates the result against the requirements set by the Regulation.The findings suggest a mixed result.The sensor delivers more accurate outputs for the smaller room sizes.Also,the accuracy does not meet the requirements,but it was found to be close to what is expected in the Regulation.Finally,the paper argues that the device is user-friendly enough to be used by non-experts for crowdsourcing indoor information and,the accuracy of its output can meet the needs of other domains such as indoor navigation and public safety.

Repurposing a deep learning network to filter and classify volunteered photographs for land cover and land use characterization

This paper extends recent research into the usefulness of volunteered photos for land cover extraction,and investigates whether this usefulness can be automatically assessed by an easily accessible,off-the-shelf neural network pre-trained on a variety of scene characteristics.Geotagged photographs are sometimes presented to volunteers as part of a game which requires them to extract relevant facts about land use.The challenge is to select the most relevant photographs in order to most efficiently extract the useful information while maintaining the engagement and interests of volunteers.By repurposing an existing network which had been trained on an extensive library of potentially relevant features,we can quickly carry out initial assessments of the general value of this approach,pick out especially salient features,and identify focus areas for future neural network training and development.We compare two approaches to extract land cover information from the network:a simple post hoc weighting approach accessible to non-technical audiences and a more complex decision tree approach that involves training on domain-specific features of interest.Both approaches had reasonable success in characterizing human influence within a scene when identifying the land use types(as classified by Urban Atlas)present within a buffer around the photograph’s location.This work identifies important limitations and opportunities for using volunteered photographs as follows:(1)the false precision of a photograph’s location is less useful for identifying on-the-spot land cover than the information it can give on neighbouring combinations of land cover;(2)groundacquired photographs,interpreted by a neural network,can supplement plan view imagery by identifying features which will never be discernible from above;(3)when dealing with contexts where there are very few exemplars of particular classes,an independent a posteriori weighting of existing scene attributes and categories can buffer against over-specificity.

Assessing quality of volunteer crowdsourcing contributions:lessons from the Cropland Capture game

Volunteered geographic information(VGI)is the assembly of spatial information based on public input.While VGI has proliferated in recent years,assessing the quality of volunteer-contributed data has proven challenging,leading some to question the efficiency of such programs.In this paper,we compare several quality metrics for individual volunteers’contributions.The data were the product of the‘Cropland Capture’game,in which several thousand volunteers assessed 165,000 images for the presence of cropland over the course of 6 months.We compared agreement between volunteer ratings and an image’s majority classification with volunteer self-agreement on repeated images and expert evaluations.We also examined the impact of experience and learning on performance.Volunteer self-agreement was nearly always higher than agreement with majority classifications,and much greater than agreement with expert validations although these metrics were all positively correlated.Volunteer quality showed a broad trend toward improvement with experience,but the highest accuracies were achieved by a handful of moderately active contributors,not the most active volunteers.Our results emphasize the importance of a universal set of expert-validated tasks as a gold standard for evaluating VGI quality.

Volunteered remote sensing data generation with air passengers as sensors

Remote sensing satellites are playing very important roles in diverse earth observation fields.However,long revisit period,high cost and dense cloud cover have been the main limitations of satellite remote sensing for a long time.This paper introduces the novel volunteered passenger aircraft remote sensing(VPARS)concept,which can partly overcome these problems.By obtaining aerial imaging data from passengers using a portable smartphone on a passenger aircraft,it has various advantages including low cost,high revisit,dense coverage,and partial anti-cloud,which can well complement conventional remote sensing data.This paper examines the concept of VPARS and give general data processing framework of VPARS.Several cases were given to validate this processing approach.Two preliminary applications on land cover classification and economic activity monitoring validate the applicability of the VPARS data.Furthermore,we examine the issues about data maintenance,potential applications,limitations and challenges.We conclude the VPARS can benefit both scientific and industrial communities who rely on remote sensing data.

Data democracy–increased supply of geospatial information and expanded participatory processes in the production of data

The global landscape in the supply,co-creation and use of geospatial data is changing very rapidly with new satellites,sensors and mobile devices reconfiguring the traditional lines of demand and supply and the number of actors involved.In this paper we chart some of these technology-led developments and then focus on the opportunities they have created for the increased participation of the public in generating and contributing information for a wide range of uses,scientific and non.Not all this information is open or geospatial,but sufficiently large portions of it are to make it one of the most significant phenomena of the last decade.In fact,we argue that while satellite and sensors have exponentially increased the volumes of geospatial information available,the participation of the public is transformative because it expands the range of participants and stakeholders in society using and producing geospatial information,with opportunities for more direct participation in science,politics and social action.

Evaluating current ethical values of OpenStreetMap using value sensitive design

The crowdsourced OpenStreetMap mapping platform is utilized by countless stakeholders worldwide for various purposes and applications.Individuals,researchers,governments,commercial,and humanitarian organizations,in addition to the engineers,professionals,and technical developers,use OpenStreetMap both as data contributors and consumers.The storage,usage,and integration of volunteered geographical data in software applications often create complex ethical dilemmas and values regarding the relationships between different categories of stakeholders.It is therefore common for moral preferences of stakeholders to be neglected.This paper investigates the integration of ethical values in OpenStreetMap using the value sensitive design methodology that examines technical,empirical,and conceptual aspects at each design stage.We use the Humanitarian OpenStreetMap Team,an existing volunteered geographic information initiative,as a case study.Our investigation shows that although OpenStreetMap does integrate ethical values in its organizational structure,a deeper understanding of its direct and indirect stakeholders’perspectives is still required.This study is expected to assist organizations that contribute to or use OpenStreetMap in recognizing and preserving existing and important ethical values.To the best of our knowledge,this is the first attempt to evaluate ethical values methodically and comprehensively in the design process of the OpenStreetMap platform.

Open land-use map:a regional land-use mapping strategy for incorporating OpenStreetMap with earth observations

A land-use map at the regional scale is a heavy computation task yet is critical to most landowners,researchers,and decision-makers,enabling them to make informed decisions for varying objectives.There are two major difficulties in generating land classification maps at the regional scale:the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time.Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens.As one of the most well-known VGI initiatives,OpenStreetMap(OSM),contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns.Whereas,most large-scale mapping approaches-including regional and national scales–confuse“land cover”and“land-use”,or build up the land-use database based on modeled land cover data-sets,in this study,we clearly distinguished and differentiated land-use from land cover.By focusing on our prime objective of mapping land-use and management practices,a robust regional land-use mapping approach was developed by integrating OSM data with the earth observation remote sensing imagery.Our novel approach incorporates a vital temporal component to large-scale land-use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work.Furthermore,our novel approach in regional scale land-use mapping produced robust results in our study area:the overall internal accuracy of the classifier was 95.2%and the external accuracy of the classifier was measured at 74.8%.

An exploratory analysis of usability of Flickr tags for land use/land cover attribution

This study explored the land use/land cover(LULC)separability by the machine-generated and user-generated Flickr photo tags(i.e.the auto-tags and the user-tags,respectively),based on an authoritative LULC dataset for San Diego County in the United States.Ten types of LULCs were derived from the authoritative dataset.It was observed that certain types of the reclassified LULCs had abundant tags(e.g.the parks)or a high tag density(e.g.the commercial lands),compared with the less populated ones(e.g.the agricultural lands).Certain highly weighted terms of the tags derived based on a term frequency–inverse document frequency weighting scheme were helpful for identifying specific types of the LULCs,especially for the commercial recreation lands(e.g.the zoos).However,given the 10 sets of tags retrieved from the corresponding 10 types of LULCs,one set of tags(all the tags located at one specific type of the LULCs)could not fully delineate the corresponding LULC due to semantic overlaps,according to a latent semantic analysis.

Geographic variability of Twitter usage characteristics during disaster events

Twitter is a well-known microblogging platform for rapid diffusion of views,ideas,and information.During disasters,it has widely been used to communicate evacuation plans,distribute calls for help,and assist in damage assessment.The reliability of such information is very important for decision-making in a crisis situation,but also difficult to assess.There is little research so far on the transferability of quality assessment methods from one geographic region to another.The main contribution of this research is to study Twitter usage characteristics of users based in different geographic locations during disasters.We examine tweeting activity during two earthquakes in Italy and Myanmar.We compare the granularity of geographic references used,user profile characteristics that are related to credibility,and the performance of Naive Bayes models for classifying Tweets when used on data from a different region than the one used to train the model.Our results show similar geographic granularity for Myanmar and Italy earthquake events,but the Myanmar earthquake event has less information from locations nearby when compared to Italy.Additionally,there are significant and complex differences in user and usage characteristics,but a high performance for the Naive Bayes classifier even when applied to data from a different geographic region.This research provides a basis for further research in credibility assessment of users reporting about disasters.

Matching authority and VGI road networks using an extended node-based matching algorithm

The amount of volunteered geographic information(VGI)has increased over the past decade,and several studies have been conducted to evaluate the quality of VGI data.In this study,we evaluate the completeness of the road network in the VGI data set OpenStreetMap(OSM).The evaluation is based on an accurate and efficient network-matching algorithm.The study begins with a comparison of the two main strategies for network matching:segment-based and nodebased matching.The comparison shows that the result quality is comparable for the two strategies,but the node-based result is considerably more computationally efficient.Therefore,we improve the accuracy of node-based algorithm by handling topological relationships and detecting patterns of complicated network components.Finally,we conduct a case study on the extended node-based algorithm in which we match OSM to the Swedish National Road Database(NVDB)in Scania,Sweden.The case study reveals that OSM has a completeness of 87%in the urban areas and 69%in the rural areas of Scania.The accuracy of the matching process is approximately 95%.The conclusion is that the extended node-based algorithm is sufficiently accurate and efficient for conducting surveys of the quality of OSM and other VGI road data sets in large geographic regions.

Data trustworthiness and user reputation as indicators of VGI quality

Volunteered geographic information(VGI)has entered a phase where there are both a substantial amount of crowdsourced information available and a big interest in using it by organizations.But the issue of deciding the quality of VGI without resorting to a comparison with authoritative data remains an open challenge.This article first formulates the problem of quality assessment of VGI data.Then presents a model to measure trustworthiness of information and reputation of contributors by analyzing geometric,qualitative,and semantic aspects of edits over time.An implementation of the model is running on a small data-set for a preliminary empirical validation.The results indicate that the computed trustworthiness provides a valid approximation of VGI quality.

Technologies to Support Community Flood Disaster Risk Reduction

Floods affect more people globally than any other type of natural hazard. Great potential exists for new technologies to support flood disaster risk reduction. In addition to existing expert-based data collection and analysis, direct input from communities and citizens across the globe may also be used to monitor, validate, and reduce flood risk. New technologies have already been proven to effectively aid in humanitarian response and recovery. However, while ex-ante technologies are increasingly utilized to collect information on exposure, efforts directed towards assessing and monitoring hazards and vulnerability remain limited. Hazard model validation and social vulnerability assessment deserve particular attention. New technologies offer great potential for engaging people and facilitating the coproduction of knowledge.

Digital Earth from vision to practice:making sense of citizen-generated content

The vision of Digital Earth(DE)put recently forward under the auspices of the International Society for DE extends the paradigm of spatial data infrastructures by advocating an interactive and dynamic framework based on near-to-real time information from sensors and citizens.This paper contributes to developing that vision and reports the results of a two-year research project exploring the extent to which it is possible to extract information useful for policy and science from the large volumes of messages and photos being posted daily through social networks.Given the noted concerns about the quality of such data in relation to that provided by authoritative sources,the research has developed a semi-automatic workflow to assess the fitness for purpose of data extracted from Twitter and Flickr,and compared them to that coming from official sources,using forest fires as a case study.The findings indicate that we were able to detect accurately six of eight major fires in France in the summer of 2011,with another four detected by the social networks but not reported by our official source,the European Forest Fire Information Service.These findings and the lessons learned in handling the very large volumes of unstructured data in multiple languages discussed in this study provide useful insights into the value of social network data for policy and science,and contribute to advancing the vision of DE.

A data fusion-based framework to integrate multi-source VGI in an authoritative land use database

Updating an authoritative Land Use and Land Cover(LULC)database requires many resources.Volunteered geographic information(VGI)involves citizens in the collection of data about their spatial environment.There is a growing interest in using existing VGI to update authoritative databases.This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique,in order to update an authoritative land use database.Each VGI data source is considered to be an independent source of information,which is fused together using Dempster-Shafer Theory(DST).The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency.Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles.The data fusion approach achieved an overall accuracy of 85.6%for the 144 features having at least two contributions when the confidence threshold was set to 0.05.Despite the heterogeneity and limited amount of VGI used,the results are promising,with 99%of the LU polygons updated or enriched.These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally。